A CORRELATION BETWEEN HEART RATE VARIABILITY AND TAP TEST
FOR DETERMINING EXERCISE PREPAREDNESS

A THESIS
Submitted to the Faculty of the School of Graduate Studies
and Research
of
California University of Pennsylvania in partial
fulfillment of the requirements for the degree of
Master of Science

by
Brendon M. Jonsson

Research Advisor, Dr. Shelly DiCesaro
California, Pennsylvania
2013

ii

iii

ACKNOWLEDGEMENTS

I would like to thank my chair, Dr. Shelly DiCesaro, and
my committee members, Bobby Sepesy and Dr. Rebecca Hess, for
their hard work and dedication towards the completion of this
document, as well as their continued support of my education
and their profession.
I would also like to thank Dr. Thomas West, Program
Director, for his continued support throughout this process.
You made this program into what it is today!
I would also like to thank Dr. Jamie Weary and Mr. Jason
Edsall for their mentorship throughout this year; you have
made it easy to do what I do, and to love doing it!
Additionally, I would like to thank Miss Carolyn
Robinson, Department Secretary, for all her help this year to
both my fellow graduates and to myself.

We could not have

done this without your guidance!
Lastly, I would like to thank Mr. Simon Wegerif,
Director, HRV Fit LTD., for cooperating with this study and
helping us obtain the iThlete devices used in this study.

iv

TABLE OF CONTENTS
Page
SIGNATURE PAGE

. . . . . . . . . . . . . . . ii

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . iii
TABLE OF CONTENTS
LIST OF TABLES
INTRODUCTION
METHODS

. . . . . . . . . . . . . . iv

. . . . . . . . . . . . . . . vi

. . . . . . . . . . . . . . . . 1

. . . . . . . . . . . . . . . . . . 5

Research Design
Subjects

. . . . . . . . . . . . . . 5

. . . . . . . . . . . . . . . . . 6

Preliminary Research. . . . . . . . . . . . . 7
Instruments . . . . . . . . . . . . . . . . 7
Procedures

. . . . . . . . . . . . . . . . 10

Hypothesis(or Hypotheses). . . . . . . . . . . 12
Data Analysis
RESULTS

. . . . . . . . . . . . . . . 13

. . . . . . . . . . . . . . . . . . 14

Demographic Data . . . . . . . . . . . . . . 14
Hypothesis Testing

. . . . . . . . . . . . . 15

Additional Findings . . . . . . . . . . . . . 17
DISCUSSION . . . . . . . . . . . . . . . . . 18
Discussion of Results . . . . . . . . . . . . 18
Conclusions . . . . . . . . . . . . . . . . 22
Recommendations. . . . . . . . . . . . . . . 23

v
REFERENCES . . . . . . . . . . . . . . . . . 24
APPENDICES . . . . . . . . . . . . . . . . . 27
APPENDIX A: Review of Literature

. . . . . . . . 28

Heart Rate Variability . . . . . . . . . . . . 29
Overview

. . . . . . . . . . . . . . . . 29

Mobile Devices

. . . . . . . . . . . . . 30

Clinical Applications . . . . . . . . . . . 33
Finger Tap Test . . . . . . . . . . . . . . 36
Current Uses . . . . . . . . . . . . . . 36
Clinical Application

. . . . . . . . . . 37

Conclusion . . . . . . . . . . . . . . . . 39
APPENDIX B: The Problem . . . . . . . . . . . . 41
Statement of the Problem . . . . . . . . . . . 42
Definition of Terms . . . . . . . . . . . . . 42
Basic Assumptions . . . . . . . . . . . . . . 43
Limitations of the Study . . . . . . . . . . . 43
Significance of the Study

. . . . . . . . . . 44

APPENDIX C: Additional Methods .

. . . . . . . . 45

Informed Consent Form (C1) . . . . . . . . . . 46
IRB: California University of Pennsylvania (C2) . . 50
Individual Data Collection Sheet (C3) . . . . . . 52
REFERENCES . . . . . . . . . . . . . . . . . 54
ABSTRACT

. . . . . . . . . . . . . . . . . 57

vi
LIST OF TABLES
Table

Title

Page

1

Subject demographic information . . . . . 15

2

Correlation: Biopac, iThlete, and FTT . . . 16

3

Correlation: iThlete testing sessions 1 & 2. 17

4

Correlation: questionnaire and measurements. 17

1

INTRODUCTION

Heart rate variability (HRV) is defined as a natural
phenomenon in which the timing between normal heart beats
varies.1

As a heart beat is recorded electronically via

electrocardiogram (ECG), there is a large spike shown on
the graph when the ventricles contract: this is known as
the QRS wave complex.

The R-R interval is the distance

between two consecutive spikes (the R wave is the highest
point on the ventricular spike, hence the R-R interval),
and this distance is what is examined when HRV is
calculated.1,2

This measurement shows the regulation of

heart rate by the autonomic nervous system.1-3 The
measurement and monitoring of that regulation has many
different clinical applications, including determining
health status, recovery, stress, fitness, and can also be
used as a guideline for exercise prescription.2-7

It is

because of those many applications that this technique can
be used in determining the exercise preparedness of an
individual, which in turn will allow for more efficient
training with improved results.

2
In determining preparedness, HRV measurements are
typically compared to a baseline value, which can be
obtained by doing a 7-day average.

Once the baseline is

obtained, the subjects will compare all new measurements to
that baseline number, which will determine their readiness
for exercise that day.

If a measurement is found to be

lower (more time between R intervals) than the average, the
subject is physiologically less prepared for exercise.

In

contrast a subject whose measurement is higher (less time
between R intervals) than baseline is physiologically well
prepared for exercise.
7

Multiple studies used this method,4-

and its benefits were shown in the significant results.
Athletes can benefit considerably from research on

HRV, which can be seen in the results of a study done by
Kiviniemi et al.,4 where subjects performing a series of
resistance training programs were found to have
statistically significant increases in training load when
HRV was used as a predictor compared to a control group and
a predefined exercise group.

Studies in which sport

specific training groups were used (such as endurance
athletes, including cycling, running, and ice hockey), also
showed significant increases in performance for individuals
who had a higher HRV measurements, and a subsequently poor
performance when HRV was found to be low.7,11-13

Using the

3
results from these studies, it shows us that HRV goes
beyond heart function, but gives us an idea as to how an
individual will perform based on their HRV measurement that
day.

Research in this area can continue to build on giving

clinicians a guide for athletic performance and intensity
guides.
The finger tap test (FTT) is a procedure that requires
the subject to tap on a designated spot as many times as
possible within a ten-second time frame.

The tests have

been used and proven valid both individually8,9 and as part
of a holistic testing method.10 The FTT is typically used to
test for autonomic brain function, such as in cases of
brain trauma, brain diseases, and general neurocognitive
testing.8-10

Additionally, the FTT was recently strongly

associated with CNS fatigue by as study that correlated
subjects FTT scores and fatigue levels recorded prior to
the testing.11

The use of this testing method for autonomic

function of the central nervous system is the reason for
correlating with HRV, a measurement of autonomic heart
function; the two measure autoregulation of important body
functions.
This study examined any possible correlations between
HRV testing and FTT testing, attempting to establish an
acceptable level between the two measurements.

4
While the aforementioned studies have employed an ECG
with an associated software program to determine HRV, a
secondary aim of the proposed study attempted to find
validity and reliability from novel technology: iThleteTM.
With this technology, one can use a halter strap heart rate
monitor which communicates wirelessly with an inexpensive
application (app) on a tablet or smart phone.

This will

allow the individual to monitor their HRV measurements and
adjust their training protocols and intensities without the
need for an expensive ECG machine.
To clarify, the primary purpose of this study was to
correlate HRV measurements taken with an electrocardiogram
to FTT scores.

A secondary purpose of this study was to

examine validity and reliability of the iThlete HRV
software application.

5

METHODS

This section includes the following subsections:
research design, subjects, instruments, procedures,
hypotheses, and data analysis.

Research Design

This observational correlation research project
explored the relationship between heart rate variability
(HRV) as measured by Biopac® electrocardiogram (ECG) and
the iThlete™ software system and the finger tap test (FTT).
Additionally, the validity and reliability of the iThlete
software system was examined in comparison with the Biopac®
ECG.

Subjects performed a finger tap test and had HRV

measurements taken with both the iThlete HR monitor and
Biopac ECG during 2 data collection sessions.1-3

Limitations of the study include:
•

Inability to fully control the subjects’ choices
outside the testing conditions, such as sleeping

6
habits, alcohol use, drug use, stress levels, and
practices and games.
•

Inability to control for outside stress levels, and
the subjects’ neuromuscular learning patterns of the
tap test conditions.

•

Inability to extrapolate beyond the college-aged
student and/or student athlete

Subjects

The subjects used in this study were 17 California
University of Pennsylvania student-athletes undertaking
strength and conditioning training with the University’s
strength and conditioning specialists.

All subjects have

completed a physical exam performed by California
University of Pennsylvania team physicians and had been
cleared for athletic activity. Furthermore, all subjects
have no cardiac or orthopedic issues that would have
precluded them from strength and conditioning training.

Inclusion criteria for this study included:
•

Current varsity athlete at California University of
Pennsylvania

7
•

Current participant in strength and conditioning
programs at California University of Pennsylvania

Exclusion criteria for this study included:
•

Any documented cardiovascular condition

•

Any person not yet medically cleared for sport
participation

Preliminary Research

Initially, the Biopac, iThlete, and tap test
procedures were tested on three volunteer athletic training
graduate students prior to the start of the study.

This

was completed to ensure that testing procedures could be
easily followed, established timing for the sessions, and
familiarized the researcher with the equipment.

Instruments

Multiple instruments were used with this project
including a Biopac ECG monitor and AcqKnowledge software
run on a standard Windows run computer, iThlete and
software run on an Apple iPad, and a finger tap test with
questionnaire. Details for each instrument follows.

8

Biopac
An electrocardiogram (ECG) and amplifier from Biopac
(BIOPAC Systems, Inc.; California, USA) had been used to
assess heart rate variability. General purpose pre-gelled
ECG electrodes (BIOPAC Systems, Inc.; California, USA) were
connected via cable leads (BIOPAC Systems, Inc.;
California, USA) from the subject to the ECG amplifier.
The testing requires a 3-lead system, where the electrode
placement would be medial to the anterior axillary fold of
the left arm and right arms and just below the sternum.
The ECG signal are sent to the computer, which is
interfaced with AcqKnowledge (BIOPAC Systems, Inc.;
California, USA) software for Windows to analyze the raw
ECG R-R interval data for the HRV measurement in
milliseconds.1-3

This data was then recorded on the

subjects data collection sheet for future comparison to the
iThlete and FTT results.

iThlete
The iThlete (HRV Fit Ltd.; UK) software was downloaded
to an iPad (Apple; California, USA).

The iThlete

communicates with a halter heart rate monitor (Cardiosport,
UK) which was secured around the patient’s chest just below

9
the xyphoid process (the notch just below the breast bone,
where the ribs converge).

The heart rate monitor sends

telemetric information to the receiver (HRV Fit Ltd., UK),
which plugs into the headphone jack of the iPad.

Once the

heart rate signal had been received, HRV was interpreted by
the open iThlete application on the iPad.

The measurement

was given as a numerical value, which was recorded on the
subjects individual data collection sheet for future
comparison between the Biopac and FTT results.

Finger Tap Test
The finger tap test (FTT) is a procedure that involves
the subject tapping with the index finger on their dominant
hand as many times as possible within a ten-second time
frame, and scored as a single numerical value.

In addition

to the tap measurement, three questions (Appendix C3) were
asked to obtain sleep quality (0-10, 0 being worst and 10
being best), mental stress level (0-10, 0 being worst and
10 being best), and how well they ate previous to testing
(0-10, 0 being worst and 10 being best).

This data was

recorded on the subjects individual data collection sheet
for future comparison between the Biopac and iThlete
measurements.

10
Procedure

Subjects were recruited openly by the primary
researcher after introduction by the strength and
conditioning staff at California University of Pennsylvania
during strength and conditioning sessions at the Hamer Hall
strength and conditioning facility. All volunteers were
participating in strength and conditioning exercise with
the Cal U strength coaches. Volunteers were asked to
participate after explanation of the project and question
and answer time.

An Informed Consent Form (Appendix C1)

was obtained from each subject prior to participation in
the study.

The study was approved by the Institutional

Review Board (Appendix C2) at California University of
Pennsylvania.

Each participant’s identity remained

confidential on the data collection sheets and did not
included identifying information during the study.
As the subjects arrived at the athletic training
facility inside Hamer Hall, they answered three questions
pertaining to sleep quality, diet quality of the previous
day, and level of mental stress, which was located on the
individual’s data collection sheet (Appendix C3).

Subjects

were then asked to sit in a dark, quite room for ten

11
minutes to rest with no physical stresses in order to
record HRV in a resting state.
Following ten minutes of rest, the subject was then
connected to the Biopac ECG and iThlete HR monitor strap.
Simultaneous measurements for HRV were performed with each
device and recorded for each subject and each device.

Each

subject performed the FTT, with the results recorded and
logged, which concluded the session for that day.
To test reliability of the iThlete software, the
procedure was repeated a second time under identical
conditions one week apart.

For example, if the original

testing took place on a Monday morning, the subjects was
asked back on the following Monday at the same time, to the
same facility.

The procedure was followed precisely, and

all steps were repeated to maintain reliable testing
conditions.
Demographic information for each subject was obtained
during the first session and recorded on the individuals
data collection sheet (Appendix C3).

All test results

(FTT, ECG HRV, iThlete HRV) were also recorded on that
individual’s anonymous data collection sheet (Appendix C3).
HRV and FTT results were recorded as a numerical value.

12
Hypotheses

The following hypotheses are based on previous
research and the researcher’s intuition:
1. There will be a strong, positive correlation between
the finger tap test and HRV measurements (Biopac and
iThlete).
2. HRV measurements from the iThlete device will be
found to have acceptable reliability using Pearson
Correlation.
3. The iThlete device will be found to correlate with
measures from the Biopac device.

Data Analysis

All data were analyzed by SPSS (version 18.0) for
Windows at an alpha level of 0.05.

The research hypothesis

was analyzed using a Pearson product correlation between
FTT results and HRV measurements from both the Biopac and
iThlete devices.

Two additional Pearson product

correlations were run: one for validity, comparing the
scores to the already valid Biopac; and one for

13
reliability, comparing the scores of the iThlete testing
sessions.

14

RESULTS

The purpose of this study was to determine if HRV
measurements were correlated with the FTT to determine if
an individual’s FTT scores would predict preparedness for
exercise. A second and third purpose of this research was
to test the reliability and validity of the iThlete
software, respectively.

The following section contains the

data collected and is divided into three subsections:
Demographic Information, Hypotheses Testing, and Additional
Findings.

Demographic Information

Eighteen healthy women and three healthy men who were
current student-athletes enrolled in California University
of Pennsylvania volunteered for this study. One female
subject was excluded from the study due to technical
difficulties during the first day of testing and a second
female subject was excluded from Hypothesis #3 testing due
to incomplete data.

The remaining subjects (n = 17,

15
Hypothesis #1 and #3 testing; n = 16, Hypothesis #2
testing) were asked their height in centimeters, weight in
kilograms, and age in years (Table 1).

Table 1. Subject demographic information.
Variable
Age (yrs)
Height
(cm)
Weight
(kg)

Minimum
18

Maximum
21

Mean
19.78

Std. Deviation
.80

158.5

190.5

171.5

2.63

56

100

74.7

10.82

Hypothesis Testing

It was hypothesized that the HRV measurements and the
FTT scores would correlate, both for the Biopac device and
the iThlete software.
A Pearson correlation coefficient was calculated for
the relationship between the subjects’ Biopac HRV
measurement and the FTT results.
correlation was found.

No significant

This research suggests that HRV

measurements from the Biopac device are not related to an
individual’s FTT results.
A Pearson correlation coefficient was calculated for
the relationship between the subjects’ iThlete score and

16
the Biopac HRV measurement.

A significant moderate

positive correlation was found, this research suggests that
the Biopac HRV measurements may predict iThlete HRV scores
approximately 50% of the time. The results of the
aforementioned correlation tests are found in Table 2.

Table 2. Pearson Product Correlation: Biopac, iThlete, and
FTT.

Biopac

iThlete

FTT

Pearson Correlation
Sig. (two-tailed)
N
Pearson Correlation
Sig. (two-tailed)
N
Pearson Correlation
Sig. (two-tailed)
N

Biopac

iThlete

1

.339*
.050
34
1

34
.339*
.050
34
-.105
.554
34

34
.209
.236
34

FTT
-.105
.554
34
.209
.236
34
1
34

It was also hypothesized that iThlete would be found
reliable between two different testing sessions by use of a
Pearson correlation.

When comparing the first and second

testing sessions, no significant correlation was found.
The results of the aforementioned hypothesis testing can be
found in Table 3.

17
Table 3.

iThlete 1

iThlete 2

Correlation: iThlete testing sessions 1 and 2.
iThlete 1
1

Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N

16
.365
.164
16

iThlete 2
.365
.164
16
1
16

Additional Findings

Additional Pearson correlation coefficients were
calculated for three questionnaire questions (sleep level,
diet quality, and stress level) against the Biopac,
iThlete, and tap test results.
were found.

No significant correlations

The results for the aforementioned

correlations can be found in Table 4.

Table 4.
Sleep

Diet

Stress

Correlation: questions and measurements.

Pearson Correlation
Sig. (two-tailed)
N
Pearson Correlation
Sig. (two-tailed)
N
Pearson Correlation
Sig. (two-tailed)
N

Biopac
.035
.845
34
-.055
.235
34
-.247
.159
34

iThlete
.297
.089
34
.235
.182
34
-.053
.764
34

FTT
.076
.670
34
-.099
.577
34
.088
.619
34

This may suggest that the subjects’ sleep, diet, and stress
levels cannot predict the HRV measurements and FTT results.

18

DISCUSSION

Discussion of Results

This study examined the relationship between heart
rate variability (HRV) and the finger tap test (FTT).

The

scores of the FTT were compared to the HRV measurements of
both the iThlete and Biopac devices to determine the
ability to use the FTT in the clinic with confidence that
it is a useful measurement tool for exercise preparedness.
Additionally, reliability of a new HRV device and software,
iThlete, was examined and compared to the already valid
Biopac device.
When correlating the FTT with the Biopac and iThlete
HRV measurements, no significant results were found.

The

FTT has been shown in the literature as being a valid tool
for CNS function12-14 yet is not related to either HRV
measurements.

This suggests the FTT score should not be

used interchangeably to determine one’s readiness for
exercise.
This study compared HRV using two different devices:
the Biopac ECG system and the iThlete software.

When the

19
two were analyzed statistically using a correlation
coefficient, there was a significant moderate positive
correlation between the two measurements.

This may suggest

the validity of the iThlete device compared to a proven
valid measure of HRV in the Biopac ECG device.15-17

While

additional research is warranted, this suggests that the
iThlete may be used in place of the Biopac ECG and the
scores can be used to determine exercise prescription.
This type of result can also be seen in the recent
validation of Polar heart rate monitors used to measure
HRV.

The initial validity was based on weak-moderate

correlations, but through additional studies and
compilation of data, strong correlations results came in
favor of the Polar monitors.18-20
The measure of HRV is not a consistently similar score
each time, but rather a dynamic score, fluctuating based on
a person’s level of fatigue, recovery, or nervous system
efficiency.21-23

It is this fluctuating relationship with

the CNS that shows the inverse relationship between the
parasympathetic (PNS) and sympathetic (SNS) nervous
systems.

As the SNS becomes more active in exercise, the

PNS conversely becomes less active, and vice versa, HRV can
be used to show this relationship and thus reflect upon the
autonomic functions of the body.23

With that information,

20
HRV looks specifically at the ratio between the two systems
to determine if the individual is well prepared for
exercise.21-23
This sparked other studies to look at HRV as an
exercise predictor, both in exercise conditions and sport
conditions.4-5,9

The results of these studies showed that

when HRV was found to be low prior to sport, that
individual did not perform as well when compared to a day
where HRV was high.9

The researchers looked at ice hockey

athletes, and measured their HRV daily and then coaches
subjectively determined their athletic performance for that
session.

The results were then correlated, and significant

findings showed when individuals had increased HRV prior to
a session, they were evaluated higher by the coaches. This
lead to the early conclusion that high HRV scores lead to
increased performance.9
This early research is what drove the current study to
look at other ways to determine exercise readiness, such as
two different studies done by Kiviniemi et al.4,5

The

authors performed two separate studies looking at HRV as a
predictor of exercise intensities.

Following the

conclusion of the exercise protocols, all training groups
were shown to have significant increases in training loads

21
compared to control groups4,5 and groups without HRV
regulated exercise.4
With a finding of moderate validity, or relationship
between Biopac and iThlete, a more economical, readily
available device may be used in order to determine ones
daily HRV score, as opposed to the gold standard
electrocardiogram.

Caution should be taken, however, as

iThlete has only been shown to be accurate approximately
50% of the time.

Given iThlete’s simple, user-friendly

interface and wireless monitor, there is no confusion due
to a complex network of wires or extra steps for analysis
on expensive equipment, such as with the Biopac.

The

Biopac required accurate placement of adhesive electrode
pads which had wires attached, leading to the ECG device
and then to the computer, and then additional software
knowledge to select the correct test to be run, set the
test parameters, and then get the measurement output.

A

person can purchase the iThlete sensor and receiver online,
the iThlete app through your mobile device (which measures,
calculates, analyzes, and stores your HRV measurement
automatically), and spend less than $100.
When looking at the use of mobile analysis of HRV, the
literature shows us that there are multiple options
available as of late.

Two different Polar devices (RS800

22
and S810/I models)18-20 and Suunto device (t6 model)20 have
recently been validated and determined reliable and
interchangeable methods of measuring HRV compared to a
computer-based ECG device, however, the cost of these
devices can be upwards of $350.

With the demand for

physiologic and HR guided exercise training continuing to
grow among the athletic population, the iThlete device
should be able to compete with these units with its selfcontained analysis and cost effective hardware.

Conclusions

Collegiate student-athletes at the NCAA Division II
level were used in this study.

It should be noted that the

results cannot be generalized to other populations, and
further research is needed in order to obtain enough
results for a greater generalization.
There were results suggesting a new method, in the
form of iThlete, had moderate validity, but additional
research is suggested to add to these findings.

Having no

significant correlation of the FTT to both Biopac and
iThlete HRV also implies that there is no relationship
between the FTT and HRV measurements on any device.

23

Recommendations

Future studies should focus their efforts on using a
full, 12-lead ECG reading for HRV in order to obtain the
most accurate measurements, but might also consider
comparing the iThlete device to other recently validated
mobile HR devices such as the Polar S810.
Future research should attempt to employ a greater
number and diversity of subjects for generalization to a
larger population.
The final suggestion for future researchers would be
to use a computerized FTT battery, rather than relying on
manual.

There are protocols available that require

specific positioning of other fingers to inhibit gross
movement in order to rely on the target finger.

In

addition to a true FTT measure, it will provide a
standardized scoring measurement that will be used across
the entire sample grouping, improving accuracy,
reliability, and feasibility for the researchers.

24

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Collier S, Kanaley J, Fernhall B, et al. Cardiac
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3.

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observation through to use of spectral analysis of heart
rate variability in ice hockey players. Acta Univ.
Palacki. 2007;37(4):17-21

10. Jouanin J, Dussault C, Peres M, Satabin P, Pierard C,

Guezennec C. Analysis of heart rate variability after a
ranger training course. Military Medicine.
2004;169(8):583-587.
11. Haglund, K. (2009). Detecting overtraining in athletes
with a finger tap test. Poster Session: Research Across
the Disciplines.
12. Gualtieri C, Johnson L. Reliability and validity of a
computerized neurocognitive test battery, CNS Vital
Signs. Archives of Clinical Neuropsychology.
2006;21:623-643
13. Hautala A, Kiviniemi A, Makikallio T, Tiinanen S,
Seppanen T, Huikuri H, Tulppo M. Muscle sympathetic
nerve activity at rest compared to exercise tolerance.
Eur Appl Physiol. 2008;102:533-538
14. Saito M, Iwase S, Hachiya T. Resistance exercise
training enhances sympathetic nerve activity furing
fatigue-inducing isometric handgrip trials. Eur J Appl
Physiol. 2009;105:225-234. DOI: 10.1007/s00421-008-08935
15. Billman G, Kukielka M. Effect of edurance exercise
training on heart rate onset and heart rate recovery
responses to submaximal exercise in animals susceptible
to ventricular fibrillation. Journal of Applied
Physiology. 2007;102:231-240
16. Kukielka M, Seals D, Billman G. Cardiac vagal modulation
of heart rate during prolonged submazimal exercise in
animals with healed myocardial infarctions: effects of
training. Am J Physiol Heart Circ Physiol.
2006;290:H1680-H1685

26
17. Chow D, Grandinetti A, Femandez E, Sutton A, Elias T,
Brooks B, Tam E. Is colcanic air pollution associated
with decreased heart-rate variability?. Heart Asia.
2010:36-41. DOI: 10.1136/ha.2009.001172
18. Wallén M, Hasson D, Theorell T, Canlon B, Osika W.
Possibilities and limitations of the polar RS800 in
measuring heart rate variability at rest. European
Journal Of Applied Physiology [serial online]. March
2012;112(3):1153-1165. Available from: SPORTDiscus with
Full Text, Ipswich, MA. Accessed May 24, 2013.
19. Porto L, Junqueira L. Comparison of Time-Domain ShortTerm Heart Interval Variability Analysis Using a WristWorn Heart Rate Monitor and the Conventional
Electrocardiogram. Pacing & Clinical
Electrophysiology [serial online]. January
2009;32(1):43-51. Available from: SPORTDiscus with Full
Text, Ipswich, MA. Accessed May 24, 2013.
20. Weippert M, Kumar M, Kreuzfeld S, Arndt D, Rieger A,
Stoll R. Comparison of three mobile devices for
measuring R–R intervals and heart rate variability:
Polar S810i, Suunto t6 and an ambulatory ECG
system. European Journal Of Applied Physiology [serial
online]. July 2010;109(4):779-786. Available from:
SPORTDiscus with Full Text, Ipswich, MA. Accessed May
24, 2013.
21. Lewis M, Short A. Exercise and cardiac regulation: what
can electrocardiographic time series tell us?. Scand J
Med Sci Sports. 2010;20:794-804
22. Bertsch K, Hagemann D, Naumann E, Schachinger H, Schulz
A. Stability of heart rate variability indicies
reflecting parasympathetic activity. Psychophysiology.
2012;49:672-682. DOI: 10.1111/j.1469-8986.2011.01341.x
23. Ebben M, Kurbatov V, Pollak. Moderating laboratory
adaptation with the use of a heart-rate variability
biofeedback device (StressEraser). Appl Psychophysiol
Biofeedback. 2009;34:245-249. DOI: 10.1007/s10484-0099086-1

27

APPENDICES

28

APPENDIX A
Review of Literature

29

REVIEW OF LITERATURE

This literature review will help determine the gaps in
the current literature for the uses of heart rate
variability (HRV) and the finger tap test (FTT).

The

review will look at different methods for measuring HRV,
including different validated mobile-based devices, and its
clinical applications.

It will also look at the uses of

the FTT and its clinical applications and possible
crossover to sport and exercise.

Heart Rate Variability

Overview
Heart rate variability (HRV) is defined as a natural
phenomenon in which the timing between normal heart beats
varies.1

As a heart beat is recorded electronically via

electrocardiogram (ECG), there is a large spike shown on
the graph when the ventricles contract: this is known as
the QRS wave complex.

The R-R interval is the distance

between two consecutive spikes (the R wave is the highest
point on the ventricular spike, hence the R-R interval),

30
and this distance is what is examined when HRV is
calculated.1,2

This measurement shows the regulation of

heart rate by the autonomic nervous system.1-3
Segerstrom and Nes4 looked to determine heart rate
variability’s relationship to one’s ability to selfregulate, or to control emotions, thoughts, and impulses.
They recruited 168 college-aged subjects to participate in
study.

Segerstrom and Nes examined food impulse and eating

behavior.

Subjects’ feelings towards different types of

food, whether or not they truly wanted to eat it, or if the
impulse was caused by the fact the food was in front of
them were among the variables examined.

The results showed

a higher change in HRV with those who ate carrots over
cookies, as well as increased effort from those who ate
carrots.

They concluded that HRV and self-regulation were

related, but more research is needed both in the lab and in
the field.4

Mobile devices
As HRV-guided exercise increases in popularity, the
amount of devices available will continue to increase.

The

devices range from wrist-worn watch devices of PolarTM and
SuuntoTM to the mobile device-based applications of
iThleteTM.

31
Weippert et al.5 looked at two different mobile devices
compared to an ECG unit for measuring HRV.

The authors

looked at the Polar S810i and Suunto t6 units, which use a
chest strap heart rate monitor to collect the heart rhythm
and are then sent to a wrist unit.
ambulatory, 5-lead design.

The ECG used was an

Intra-class correlation

coefficients were obtained for the three comparisons at a
95% confidence interval (Suunto vs. Polar [.999]; Polar vs.
ECG [.996]; Suunto vs. ECG [.998]).5

With the results, it

can be said that the three units can be interchanged for
HRV testing.

The authors noted that it is not recommended

to use different devices for intra-individual studies in
order to maintain testing reliability.5
The Polar S810 was also looked at in a study done by
Grossi Porto and Junqueira,6 where they used the wrist worn
Polar S810 and compared it to a conventional ECG set up.
33 individuals (15 men, 18 women; ages 18-42) were
recruited for this study.
12-lead ECG.

The Polar S810 was compared to a

Using the Bland-Altman method and plot, the

authors determined significant level of agreement between
the two measurements.

They further concluded that the use

of the Polar S810 could be used for short-term measurement
of HRV, but any measurement longer than 10-minutes would
have to be examined further.6

32
In a study done by Wallen et al.,7 the polar RS800 was
examined in comparison to a traditional ECG unit.

There

was a total of 341 participants (139 men, mean age 52; 202
women, mean age 53).7

The authors took simultaneous

measurements of both the ECG and the Polar devices, of
which were stored on a computer for analysis.

Intra-class

correlation coefficients at 95% confidence were found for
each age group, each gender, and all data total.

It was

found that all age groups and genders had an average ICC of
.930, with men averaging .968 and women averaging .898.
All gender and age combinations were found significant with
the exception of women over the age of 60 (there were no
known reasons for this at the time of study).

The data

suggest the use of this new device on all populations with
the exception of women over 60 years old.7
In a study done by Cassirame et al.8 set out to examine
the accuracy of the Minicardio system for assessing resting
heart rate and HRV compared to a standard ECG recording.
On 15 young participants, it was found that the heart rate
was accurate with no artifacts between the two devices.
Pearson coefficients were found to be 1.0 and .99 for both
mean R-R interval and RMSSD, respectively.8

They concluded

that the use of Minicardio systems was to be encouraged and
a portable recorder of heart rate and HRV.

33
Clinical applications
HRV has been examined in recent literature for its
uses in sport exercise and performance, but additionally in
its ability to predict fitness, sleep, and even guide neckshoulder pain treatment.
Kiviniemi et al.9 examined the use of HRV as a daily
exercise prescription tool.

In a study done in 2007, the

authors recruited twenty-six males to participate in this
study (8 in predefined training group, 9 in HRV determined
group, and 9 in control group).

The HRV group did either

high intensity (high HRV) or low intensity/rest (low
HRV/low HRV for consecutive days), while the predetermined
group did a set intensity, and the control group
participated in no exercise.

Results showed the HRV group

having a significant increase in both training load and
oxygen consumption (VO2max), with no significant changed in
VO2max, but a significant increase in training load.

There

were no changes reported in the control group.9
Kiviniemi10 put together another study in 2010, where
he and the co-authors used both men and women, and followed
similar methods as their previous study from 2007.

This

study contained 4 groups, however; control, predetermined
intensity, and then two HRV groups: HRV determined and HRV
high intensity only.

They came to the same conclusion as

34
their previous study, where there were significant training
load increases in the HRV determined group compared to all
other groups.

They also determined that women gained a

significant fitness improvement at a lower training load.10
Cipryan et al.11 looked at the use of HRV in
conjunction of coaches’ performance evaluations to
determine the usefulness of HRV in performance of hockey
players in 2007.

The subjects filled out a questionnaire

prior to weekly HRV measurements inquiring about the
previously training load, sleep duration and quality, and
the athlete’s perceived level of health.

The coaches would

then evaluate each player on a scale of 1-10 (10 being the
best).

The results showed that players with the highest

HRV ratings were also the ones who had the most consistent
ratings from their coaches.

Also, the players with the

lowest HRV scores showed to correspond with the lowest
evaluations from the coaches, which is just as
significant.11
Researchers grouped ice hockey players together, and
monitored their HRV and skills in a study done in 2010 by
Cipryan and Stejskal.12

They set out to determine if

grouping similarly monitored HRV individuals together would
increase training effectiveness, reduce injury, and prevent
overtraining.

Upon the results, they showed that the

35
individuals with the same ANS activity benefited more from
training, and suggest that team based on ANS monitoring and
similar ANS reports would be beneficial.12
Sloan et al.13 used exercise as an attempt to change
the cardiac autonomic regulation variables in sedentary
young adults.

The authors had a total of 149 subjects

using either aerobic or strength training in attempts to
influence aerobic capacity, heart rate, and HRV.

Following

12 weeks of protocol, they saw a significant change in the
aerobic group only, including an increase in both aerobic
capacity and HRV, and a decrease in heart rate.

It was

also interesting to note that the changes were only seen in
men, and all levels returned to pre-testing levels
following a 4-week deconditioning session.13
Military training was examined in this study by
Jouanin et al.,14 with emphasis put on HRV and recovery,
fatigue, and performance, and blood tests were done to
examine hormone levels.

The subjects were put through a

15-week Ranger training camp, where they were expected to
perform anaerobic, aerobic, and stressful tasks with
compounded fatigue, meaning recovery was never possible.
HRV increased significantly following the tests, suggesting
that increased fatigue brings a subsequent increase in

36
parasympathetic activity rather than a decrease in
sympathetic activity.14
Hallman et al.15 sought out to use HRV as a biofeedback
guide to treat stress related chronic neck/shoulder pain in
twenty-four otherwise healthy subjects.

The researchers

grouped 12 participants in both a control group and an HRV
biofeedback group for 10 weekly sessions.

The biofeedback

group showed an increased perception of health compared to
the control group following the 10 sessions, suggesting HRV
as an effective biofeedback marker.15

Finger Tap Test

The Finger tap Test (FTT) is a testing procedure in
which a subject uses their dominant hand index finger to
tap rapidly on a device for a set amount of time, typically
10 seconds.

This procedure has many different uses and

clinical applications, which will be examined further in
the review of literature below.

Overview
In the book A Compendium of Neuropsychological Tests:
Administration, Norms, and Commentary, Strauss goes on to
describe the uses and functions of the FTT.16

In addition

37
to the FTT being effected by brain trauma, dementia, or
motor dysfunctions of cerebellar or cerebral origins, the
FTT results can be effected by chronic pain, attention,
fatigue, or impaired ability to focus.16

The author also

goes on to explain further that not only should finger
tapping speed be examined, but the tapping pattern as well.
It is mentioned that individuals with traumatic brain
injuries most commonly have an abnormal pattern rather than
a decreased tapping speed, depending on the severity of the
injury.16

Clinical Applications
In both a 1997 qualitative and quantitative study done
by Prigatano and Hoffmann,17 30 patients were used with the
use of the FTT to analyze brain dysfunction.

Fifteen brain

dysfunction patients and 15 normal controls were put
through the protocol of the Halstead Finger Tapping Test.
Upon conclusion, the authors determined that the brain
dysfunction subjects had not only a slower tapping rate,
but an abnormal pattern compared to the normal control
subjects.17
Prigatano, Johnson, and Gale18 went on to examine the
effects of the Halstead Finger Tapping Test in individuals
with traumatic brain injuries.

In this study done in 2004,

38
the authors used subjects with an average of 18.5 years
post-trauma, and noted that all subjects had normal or
near-normal tapping times.18

Subjects were asked to perform

the FTT while undergoing a functional magnetic resonance
image (fMRI).

Following the imaging, it was seen that

healthy controls showed a greater brain activation.

The

authors concluded that different level of brain activation
can be seen in individuals suffering from traumatic brain
injury even when performance is within normal limits.18
Gualtieri and Johnson19 performed a validation study of
a computerized testing battery called CNS Vital Signs
(CNSVS), which is used to measure neurocognitive clinical
screenings.

The test is a combination of 7 other subtests:

verbal and visual memory, finger tapping, symbol digit
coding, the Stroop Test, a test of shifting attention, and
the continuous performance test.

The testing was found to

be highly reliable between test-retest procedures, and
additionally was found to be valid compared to the results
of other testing batteries such as TOVA (Tests of Variables
of Attention).

Furthermore, they concluded that

computerized testing methods showed a more consistent
correlation coefficient, and have been shown to be more
reliable with traumatic brain injuries, dementia, and
ADHD.19

39
In an article by Emeljonavas, Poderys, and
Venskaityte,20 70 boys between the ages of eleven and
fourteen were examined for the effect of variable training
on the dynamics of muscular, cardiovascular, and central
nervous system (CNS).

They used the FTT in order to

determine the CNS involvement.

They study concluded that

boys ages 13-14 years had significantly increased CNS
indices compares to the boys ages 11-12 years.20
In a study done by Haglund21 out of the National Sports
Center in St. Paul, MN, fourteen Division III collegiate
athletes were asked to perform the FTT daily.

In addition,

they logged their perceived fatigue level and the
difficulty of the previous day’s workout.

Upon completion

of the analysis, the researcher found that CNS fatigue can
be measured using the FTT and additionally, CNS fatigue may
be affected by workout difficulty.21

Conclusion

In conclusion, the literature examined many different
applications for both HRV measurements FTT results.

In two

studies done by Kiviniemi9,10, both the importance and
significance of HRV testing and exercise adaptation were
outlined for clinicians dealing with athletes.

In both

40
studies, HRV guided exercise intensity groups were shown to
have statistically significant higher training loads
compared to all other groups, including HR high intensity
only group, control group, and non-HRV exercise group.
Cipryan and Stejskal12 also examined HRV with performance,
but rather that using exercise, the authors paired the
measurement with sport performance.

The results went to

suggest that athletes with higher HRV measurements had
higher performance ratings and athletes that had low HRV
measurements subsequently had lower performance ratings..
Using the HRV guided method, athletes can train more
efficiently and gain better training outcomes, both in
sport and exercise.
As the FTT was examined in literature, it was
conclusive that the test was reliable and valid for
measuring CNS efficiency and fatigue.20,21

With heart rate

and HRV being autonomic functions;1-3 this is significant
that it may also be related to exercise preparedness.

The

study done by Haglund21 showed that FTT was strongly
correlated with CNS fatigue and exercise intensity.

This

could be an important tool for clinicians to use at the
conclusion of exercise to determine its difficulty.

41

APPENDIX B
The Problem

42

STATEMENT OF THE PROBLEM

Literature has extensively covered the topic of heart
rate variability in terms of exercise response, prediction,
and determination over a variety of subjects, including
college-aged adults, sport teams, and even Army forces in
order to uncover significance of heart rate variability in
terms of training.

One area that has been overlooked,

however, is the use of heart rate variability to determine
the readiness of an individual for training or exercise.
The research being proposed will help to unveil additional
findings that can help clarify the effectiveness.

Definition of Terms
The following definitions of terms will be defined for
this study:
1) Heart rate variability – the body’s natural phenomenon
resulting in a fluctuation of timing between heart
beats
2) Finger tap test – a testing battery that examines the
efficiency of the autonomic nervous system by

43
measuring the number of taps in a 10-second time frame
from the patient’s dominant index finer

Basic Assumptions
The following are basic assumptions of this study:
1)

The information collected from the subjects will be
able to be generalized to similar athletes.

2)

The subjects will be honest when they complete their
demographic sheets.

3)

The equipment being used is appropriate and valid for
measuring heart rate variability

4)

The equipment was working properly and calibrated
correctly.

Limitations of the Study
The following are possible limitations of the study:
1)

The subjects may not show consistency in their
preparedness questionnaire.

2)

The training sessions being performed may not be
sufficient to test the hypothesis.

Delimitations of the Study
The following are possible delimitations of the study:

44
1)

The subjects were collegiate athletes from California
University of Pennsylvania.

2)

The subjects were that of a convenience sample.

Significance of the Study
This study will provide data to allow a clinician the
ability to prescribe exercise based on the physiological
status of the patient.

This, in turn, will provide a

better training experience for the patient, as well as
provide a potential for increased performance and larger
training gains.
Not only will patients be immediately benefited from
this research, but new technology could become available
that is more economical and widely available to the general
public.

This will allow patients to obtain their own

readings and direct their own training without the need for
a professional to guide them.
With the results of this study, athletes will be able
to train more efficiently.

Doors will also be opened for

potential further application of heart rate variability and
performance, program prescription, and exercise response.

45

APPENDIX C
Additional Methods

46

APPENDIX C1
Informed Consent Form

47

48

49

50

APPENDIX C2
Institutional Review Board –
California University of Pennsylvania

51
Institutional Review Board
California University of Pennsylvania
Morgan Hall, Room 310
250 University Avenue
California, PA 15419
instreviewboard@calu.edu
Robert Skwarecki, Ph.D., CCC-SLP,Chair

Dear Mr. Jonsson:
Please consider this email as official notification that your proposal titled
"A correlation between heart rate variability and tap test for determining
exercise preparedness” (Proposal #12-062) has been approved by the
California University of Pennsylvania Institutional Review Board as
submitted.
The effective date of the approval is 3-29-2013 and the expiration date is 328-2014. These dates must appear on the consent form .
Please note that Federal Policy requires that you notify the IRB promptly
regarding any of the following:
(1) Any additions or changes in procedures you might wish for your study
(additions or changes must be approved by the IRB before they are
implemented)
(2) Any events that affect the safety or well-being of subjects
(3) Any modifications of your study or other responses that are necessitated
by any events reported in (2).
(4) To continue your research beyond the approval expiration date of 3-282014 you must file additional information to be considered for continuing
review. Please contact instreviewboard@calu.edu
Please notify the Board when data collection is complete.
Regards,
Robert Skwarecki, Ph.D., CCC-SLP
Chair, Institutional Review Board

52

Appendix C3
Individual Data Collection Sheet

53
Individual Data Collection Sheet

Subject #: _____________

Year school: ____________

Gender: ________________

Height: _________________

Age: ___________________

Weight: _________________

Subject:
Sleep quality?
Diet quality?
Stress level?
Biopac HRV
Ithlete HRV
Tap test

Session 1

Session 2

54

REFERENCES

1.

Smith DL, Fernhall B. Advanced Cardiovascular Exercise
Physiology: Advanced Exercise Physiology Series.
Champagne: Human Kinetics; 2011.

2.

Lewis M, Short A. Exercise and Cardiac Regulation: What
Can Electrocardiographic Time Series Tell Us? Lewis &
Short Exercise and Cardiac Regulation. Scandinavian
Journal of Medicine & Science In Sports [serial online].
December 2010;20(6):794-804. Available from: SPORTDiscus
with Full Text, Ipswich, MA. Accessed June 5, 2013.

3.

Niccolini P, Ciulla M, Asmundis C, Margini F, Brugada P.
The Prognostic Value of Heart Rate Variability in the
Elderly, Changing the Perspective: From Sympathovagal
Balance to Chaos Theory. Pacing & Clinical
Electrophysiology [serial online]. May 1012;35(5):622638. Available from: SPORTDiscus with Full Text,
Ipswich, MA. Accessed June 5, 2013.

4.

Segerstrom S, Nes L. Heart rate variability reflects
self-regulatory strength, effort, and fatigue.
Psychological Science. 2007;18(3):275-281

5.

Weippert M, Kumar M, Kreuzfeld S, Arndt D, Reiger A,
Stoll R. Comparison of Three Mobile Devices for
Measuring R-R Intervals and Heart Rate Variability:
Polar S810i, Suunto t6 and an Ambulatory ECG system.
European Journal of Applied Physiology. 2010;109:779-786

6.

Grossi Porto L, Junqueira L. Comparison of Time-Domain
Short-Term Heart Interval Variability Analysis Using a
Wrist-Worn Heart Rate Monitor and the Conventional
Electrocardiogram. PACE. January 2009;32:43-51

7.

Wallen M, Hasson D, Theorell T, Canlon B, Osika W.
Possibilities and Limitations of the Polar RS800 in
Measuring Heart Rate Variability at Rest. European
Journal of Applied Physiology. 2012;112:1153-1165

55
8.

Cassorame J, Stuckey M, Sheppard F, Tordi N. Accuracy of
the Minicardio System for Heart Rate Variability
Analysis Compared to ECG. Journal of Sports Medicine and
Physical Fitness. June 2013;53(3):248-254

9.

Kiviniemi A, Hautala A, Kinnunen H, Tulppo M. Endurance
training guided individually by daily heart rate
variability measurements. Eur J Appl Phyiol.
2007;101:743-751. doi: 10.1007/s00421-007-0552-2

10. Kiviniemi A, Hautala A, Kinnunen H, Nissila J, Virtanen
P, Karjalainen J, Tulppo M. Daily exercise prescription
on the basis of HR variability among men and women.
Medicine and science in sports and exercise. 2010;13551363. doi: 10.1249/MSS.0b013e3181cd5f39
11. Cipryan L, Stejskal, Bartakova O, Botek M, Cipryanove H,
Jakubec A, Petr M, Rehova I. Autonomic nervous system
observation through to use of spectral analysis of heart
rate variability in ice hockey players. Acta Univ.
Palacki. Olomuc., Gymn. 2007;37(4):17-21
12. Cipryan L, Stejskal. Individual training in team sports
based on autonomic nervous system activity assessments.
Med Sport. 2010;14(2):56-62.
13. Sloan R, Shapiro P, DeMeersman R, Bagjella E, Brondolo
E, McKinley P, Slavov I, Fang Y, Myers M. The effect of
aerobic training on cardiac autonomic regulation in
young adults. American Journal of Public Health.
2009;99(5):921-928
14. Jouanin J, Dussault C, Peres M, Satabin P, Pierard C,
Guezennec C. Analysis of heart rate variability after a
ranger training course. Military Medicine.
2004;169(8):583-587
15. Hallman D, Olsson E, von Scheele B, Melin L, Lyskov.
Effects of heart variability biofeedback in subjects
with stress-related chronic neck pain: a pilot study.
Appl Psychophysiol Biofeedback. 2010;36:71-80. DOI:
10.1007/s10484-011-9147-0
16. Strauss, E. (2006). A compendium of neuropsychological
tests: Administration, norms, and commentary. (3rd ed.).
New York, NY: Oxford University Press.

56
17. Prigatano G, Hoffmann B. Finger tapping and brain
dysfunction: a qualitative and quantitative study.
Barrow Quarterly. 1997;13(4)
18. Prigatano G, Johnson S, Gale S. Neuroimaging correlates
of the halstead finger tapping test several years posttraumatic brain injury. Brain Inj. 2004;18(7):661-669
19. Gualtieri C, Johnson L. Reliability and validity of a
computerized neurocognitive test battery, CNS Vital
Signs. Archives of Clinical Neurophychology.
2006;21:623-643
20. Emeljanovas A, Poderys J, Venskaityte E. Impact of
training in sports games and dyclic sports events on
cardiovascular system, motor, and sensomotor abilities
of 11-14 year-old boys. Ugdymas - Kuno Kultura Sportas. 2009;72(1):33-39
21. Haglund, K. (2009). Detecting overtraining in athletes
with a finger tap test. Poster Session: Research Across
the Disciplines.

57

ABSTRACT
TITLE:

A Correlation Between Heart Rate Variability
and Tap Test for Determining Exercise
Preparedness

RESEARCHER:

Brendon M. Jonsson

ADVISOR:

Dr. Shelly DiCesaro

RESEARCH TYPE: Masters Thesis
PURPOSE:

The purpose of this study is to correlate
HRV measurements taken with an
electrocardiogram to FTT scores. A
secondary purpose of this study is to
examine validity and reliability of the
iThlete HRV software application through
additional correlations.

METHOD:

An observational correlation research
project explored the relationship between
heart rate variability and finger tap test.
Subjects were 17 student-athletes from
California University of Pennsylvania. All
subjects participated in two testing
sessions obtaining HRV (Biopac and iThlete)
and FTT results, in addition to sleep, diet,
and stress levels at time of measurement.

FINDINGS:

Pearson correlation coefficients showed
significant relationships for Biopac vs.
iThlete (r = .339, p = .05), no significant
results for both Biopac vs. FTT and iThlete
vs. FTT. Pearson correlation coefficient
for reliability of iThlete measurements
session one versus session two were also had
no significant findings. Additionally,
there were no significant relationships
found between any of the testing
measurements and the questionnaire
responses.

58
CONCLUSION:

Results suggest iThlete has moderate level
of validity, yet further research is needed
to determine reliability of device. FTT
should not be used as exercise predictor
based on results of this study. Suggest
further research with increased subjects and
measurements, in addition to using
computerized FTT battery over manual method.